Math @ Duke

Publications [#335534] of Ingrid Daubechies
Papers Published
 Daubechies, I; Teschke, G; Vese, L, Iteratively solving linear inverse problems under general convex constraints,
Inverse Problems and Imaging, vol. 1 no. 1
(January, 2007),
pp. 2946, American Institute of Mathematical Sciences (AIMS) [doi]
(last updated on 2019/05/24)
Abstract: © 2007 AIMSciences. We consider linear inverse problems where the solution is assumed to fulfill some general homogeneous convex constraint. We develop an algorithm that amounts to a projected Landweber iteration and that provides and iterative approach to the solution of this inverse problem. For relatively moderate assumptions on the constraint we can always prove weak convergence of the iterative scheme. In certain cases, i.e. for special families of convex constraints, weak convergence implies norm convergence. The presented approach covers a wide range of problems, e.g. Besov– or BV–restoration for which we present also numerical experiments in the context of image processing.


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